"A uniform bound on the operator norm of sub-Gaussian random matrices and its applications," with Hyugsik Roger Moon, Econometric Theory, 38, 2022, 1073–1091
Abstract For an $N \times T$ random matrix $X(\beta)$ with weakly dependent uniformly sub-Gaussian entries $x_{it}(\beta)$ that may depend on a possibly infinite-dimensional parameter $\beta\in\mathbf{B}$, we obtain a uniform bound on its operator norm of the form $\mathbb{E} \sup_{\beta \in \mathbf{B}} \|X(\beta)\| \le CK\left( \sqrt{\max(N,T)} + \gamma_2(\mathbf{B}, d_{\mathbf{B}}) \right)$, where $C$ is an absolute constant, $K$ controls the tail behavior of (the increments of) $x_{it}(\cdot)$, and $\gamma_2(\mathbf{B}, d_{\mathbf{B}})$ is Talagrand’s functional, a measure of multi-scale complexity of the metric space $(\mathbf{B}, d_{\mathbf{B}})$. We illustrate how this result may be used for estimation that seeks to minimize the operator norm of moment conditions as well as for estimation of the maximal number of factors with functional data.

Working papers

“Bias correction for quantile regression estimators,” (Jan 2024), with Bulat Gafarov and Kaspar Wüthrich, resubmitted to Journal of Econometrics. Previous drafts: Dec 2022, Apr 2021
Abstract We study the bias of classical quantile regression and instrumental variable quantile regression estimators. While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases. We derive a higher-order stochastic expansion of these estimators using empirical process theory. Based on this expansion, we derive an explicit formula for the second-order bias and propose a feasible bias correction procedure that uses finite-difference estimators of the bias components. The proposed bias correction method performs well in simulations. We provide an empirical illustration using Engel’s classical data on household expenditure.

“Nonparametric inference on counterfactuals in first-price auctions,” (June 2022), with Pasha Andreyanov
Abstract In a classical model of the first-price sealed-bid auction with independent private values, we develop nonparametric estimation and inference procedures for a class of policy-relevant metrics, such as total expected surplus and expected revenue under counterfactual reserve prices. Motivated by the linearity of these metrics in the quantile function of bidders’ values, we propose a bid spacings-based estimator of the latter and derive its Bahadur-Kiefer expansion. This makes it possible to construct exact uniform confidence bands and assess the optimality of a given auction rule. Using the data on U.S. Forest Service timber auctions, we test whether setting zero reserve prices in these auctions was revenue maximizing.

“Bias correction and uniform inference for the quantile density function,” (July 2022)
Abstract For the kernel estimator of the quantile density function (the derivative of the quantile function), I show how to perform the boundary bias correction, establish the rate of strong uniform consistency of the bias-corrected estimator, and construct the confidence bands that are asymptotically exact uniformly over the entire domain $[0, 1]$. The proposed procedures rely on the pivotality of the studentized bias-corrected estimator and known anti-concentration properties of the Gaussian approximation for its supremum.

“Efficient counterfactual estimation in semiparametric discrete choice models: a note on Chiong, Hsieh, and Shum (2017),” (Dec 2021)
Abstract I suggest an enhancement of the procedure of Chiong, Hsieh, and Shum (2017) for calculating bounds on counterfactual demand in semiparametric discrete choice models. Their algorithm relies on a system of inequalities indexed by cycles of a large number $M$ of observed markets, and hence seems to require computationally infeasible enumeration of all such cycles. I show that such enumeration is unnecessary because solving the “fully efficient” inequality system exploiting cycles of all possible lengths $K=1,\dots,M$ can be reduced to finding the length of the shortest path between every pair of vertices in a complete bidirected weighted graph on $M$ vertices. The latter problem can be solved using the Floyd–Warshall algorithm with computational complexity $O(M^3)$, which takes only seconds to run even for thousands of markets. Monte Carlo simulations illustrate the efficiency gain from using cycles of all lengths, which turns out to be positive, but small.

Work in progress

“Estimation and inference in panel models with attrition and refreshment," with Jinyong Hahn, Pierre Hoonhout, Arie Kapteyn, and Geert Ridder

“Closed-form estimation and inference in panel models with attrition and refreshment," with Lidia Kosenkova

"The association between risk-adjusted wound healing rates and long-term outcomes in a network of U.S. wound care clinics," with Andrew Becker, Soeren Mattke, Mary Sheridan, and William Ennis

"Medical costs and caregiven burden of delivering disease-modifying Alzheimer's treatments with different duration and route of administration," with Tabasa Ozawa and Soeren Mattke

“Nonparametric welfare analysis with additively separable heterogeneity"

“Dyadic quantile regression," with Hyungsik Roger Moon

Graduate courses

@UCLA (Winter 2024):

Machine learning for economists ML pipeline. Linear models. Regularization: lasso, ridge. Logistic regression. Decision trees and random forests. Imbalanced data: SMOTE. Neural networks. Clustering and principal component analysis. Bagging, boosting, and ensemble methods. Large language models. Reinforcement learning.

@USC (2017-2022):

Big data econometrics
Probability and statistics
Time series analysis
Economics of financial markets

@New Economic School (2012-2014):

Econometrics I, II, III
Mathematics for economists I, II
Game theory
Empirics of financial markets
Probability theory

Undergraduate courses

Principles of microeconomics (USC 2017)


USC Dornsife Center for Economic and Social Research
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Los Angeles, CA 90089

Email: franguri [at] usc [dot] edu